In a systematic review of 84 articles on AI in heart disease, deep learning models were employed in 63% of studies for diagnosis tasks, while machine learning models were utilized in 37%.
Systematic Review (n=84)
Does artificial intelligence improve the diagnosis and prognosis of heart diseases?
Artificial intelligence, particularly deep learning and machine learning models, shows significant potential in improving the diagnosis and prognosis of heart diseases, though standardized frameworks and explainable AI are needed for clinical integration.
Absolute Event Rate: 63% vs 37%
The integration of artificial intelligence (AI) into the diagnosis and prognosis of heart diseases is transforming cardiovascular and cardiac healthcare, improving predictive accuracy, and personalizing treatment plans. This review presents a novel contribution by providing a comprehensive overview of both diagnosis and prognosis in heart diseases through AI, covering ML and DL models. Following the PRISMA guidelines, a total of 84 recent research articles sourced from significant journals are reported. A bibliometric analysis using the VOSviewer tool was performed to map the impact of AI, enabling a detailed examination of academic connections and contributions. The findings reveal that DL models were employed 63% for diagnosis tasks, while ML models were utilized in 37% of the studies. Key recommendations include the incorporation of essential model evaluation metrics, as clinical validation indicators, integrating explainable artificial intelligence (XAI) to improve the transparency and interpretability of models, and adopting standardized frameworks to enable smooth clinical integration. This review highlights the potential of AI to improve cardiac and cardiovascular diagnosis and prognosis, providing an overview of its strengths, limitations, challenges and the possible application as AI-driven tools in patient monitoring and to support specialists in the decision-making process.
Tapia-Mendez et al. (Wed,) conducted a systematic review in Heart diseases (n=84). Artificial intelligence (ML and DL models) was evaluated on Model utilization for diagnosis tasks. In a systematic review of 84 articles on AI in heart disease, deep learning models were employed in 63% of studies for diagnosis tasks, while machine learning models were utilized in 37%.
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